class_docstr: Collects samples for one agent in one trajectory (episode).
  
  The agent may be part of a multi-agent environment. Samples are stored in
  lists including some possible automatic "shift" buffer at the beginning to
  be able to save memory when storing things like NEXT_OBS, PREV_REWARDS,
  etc.., which are specified using the trajectory view API.
docstr: Caches the numpy version of the key in the buffer dict.
docstr_tok: ["Caches","the","numpy","version","of","the","key","in","the","buffer","dict","."]
code: def _cache_in_np(
      self, cache_dict: Dict[str, List[np.ndarray]], key: str
  ) -> None:
      """Caches the numpy version of the key in the buffer dict."""
      if key not in cache_dict:
          cache_dict[key] = [
              _to_float_np_array(d) for d in self.buffers[key]
          ]
  
imports: import copy
  import logging
  import math
  from typing import Any, Dict, List, Optional
  import numpy
  import tree
  from gymnasium.spaces import Space
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.view_requirement import ViewRequirement
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.spaces.space_utils import flatten_to_single_ndarray, get_dummy_batch_for_space
  from ray.rllib.utils.typing import EpisodeID, EnvID, TensorType, ViewRequirementsDict
  from ray.util.annotations import PublicAPI
label_desc: Use image node_id 11 for calling the AgentCollector obj's underlying member method code with example usage: obj._cache_in_np(cache_dict, key) without return types